Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis

TL;DR

This paper presents a multimodal deep learning approach that combines medical images and clinical reports to improve disease analysis, classification, and localization.

Contribution

The study introduces a novel multi-modal fusion model that effectively integrates image and text features for enhanced disease understanding.

Findings

Outperforms existing models in disease classification accuracy

Achieves superior lesion localization results

Generates more accurate clinical descriptions

Abstract

In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features interact and integrate effectively through the designed multi-modal fusion layer to realize the joint representation learning of image and text. In the empirical study, we selected a large medical image database covering a variety of diseases, combined with corresponding clinical reports for model training and validation. The proposed multimodal deep learning model demonstrated substantial superiority in the realms of disease classification, lesion localization, and clinical description generation, as evidenced by the experimental results.